Overview

Dataset statistics

Number of variables11
Number of observations40768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory88.0 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:05:38.915871
Analysis finished2020-08-25 00:05:59.571071
Duration20.66 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

X1
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4995133193110154
Minimum0.0
Maximum1.0
Zeros12
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:05:59.622178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05000000075
Q10.2529999912
median0.4990000129
Q30.7480000257
95-th percentile0.9499999881
Maximum1
Range1
Interquartile range (IQR)0.4950000346

Descriptive statistics

Standard deviation0.2881766115
Coefficient of variation (CV)0.5769147696
Kurtosis-1.193385353
Mean0.4995133193
Median Absolute Deviation (MAD)0.2479999661
Skewness0.002974340935
Sum20364.159
Variance0.08304575943
2020-08-25T00:05:59.728145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.02600000054660.2%
 
0.3779999912640.2%
 
0.01899999939600.1%
 
0.8669999838590.1%
 
0.1439999938590.1%
 
0.6800000072590.1%
 
0.4370000064580.1%
 
0.9129999876580.1%
 
0.7319999933570.1%
 
0.3610000014570.1%
 
0.8109999895560.1%
 
0.3030000031550.1%
 
0.3709999919540.1%
 
0.3109999895540.1%
 
0.1140000001540.1%
 
0.1220000014540.1%
 
0.4810000062540.1%
 
0.3889999986540.1%
 
0.9060000181530.1%
 
0.01400000043530.1%
 
0.6380000114530.1%
 
0.398999989530.1%
 
0.4819999933530.1%
 
0.5780000091530.1%
 
0.6330000162530.1%
 
Other values (976)3936596.6%
 
ValueCountFrequency (%) 
012< 0.1%
 
0.001000000047360.1%
 
0.002000000095450.1%
 
0.003000000026360.1%
 
0.00400000019370.1%
 
0.004999999888460.1%
 
0.006000000052360.1%
 
0.007000000216410.1%
 
0.00800000038370.1%
 
0.008999999613440.1%
 
ValueCountFrequency (%) 
1220.1%
 
0.9990000129350.1%
 
0.9980000257390.1%
 
0.996999979430.1%
 
0.9959999919390.1%
 
0.9950000048500.1%
 
0.9940000176340.1%
 
0.9929999709480.1%
 
0.9919999838380.1%
 
0.9909999967440.1%
 

X2
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5021512461662525
Minimum0.0
Maximum1.0
Zeros20
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:05:59.844779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05200000107
Q10.2529999912
median0.503000021
Q30.753000021
95-th percentile0.9520000219
Maximum1
Range1
Interquartile range (IQR)0.5000000298

Descriptive statistics

Standard deviation0.2883271009
Coefficient of variation (CV)0.5741837804
Kurtosis-1.19668029
Mean0.5021512462
Median Absolute Deviation (MAD)0.25
Skewness-0.003141231683
Sum20471.702
Variance0.08313251709
2020-08-25T00:05:59.950427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.3880000114640.2%
 
0.7009999752600.1%
 
0.925999999600.1%
 
0.4850000143590.1%
 
0.4880000055590.1%
 
0.1829999983580.1%
 
0.04500000179570.1%
 
0.8019999862570.1%
 
0.3670000136560.1%
 
0.2689999938550.1%
 
0.6850000024550.1%
 
0.5989999771550.1%
 
0.7639999986550.1%
 
0.3540000021550.1%
 
0.3339999914550.1%
 
0.5379999876540.1%
 
0.8100000024540.1%
 
0.6090000272540.1%
 
0.4799999893540.1%
 
0.1019999981540.1%
 
0.8960000277540.1%
 
0.9909999967540.1%
 
0.3370000124540.1%
 
0.4550000131530.1%
 
0.1140000001530.1%
 
Other values (976)3937096.6%
 
ValueCountFrequency (%) 
020< 0.1%
 
0.001000000047380.1%
 
0.002000000095330.1%
 
0.003000000026410.1%
 
0.00400000019420.1%
 
0.004999999888300.1%
 
0.006000000052310.1%
 
0.007000000216410.1%
 
0.00800000038320.1%
 
0.008999999613380.1%
 
ValueCountFrequency (%) 
115< 0.1%
 
0.9990000129410.1%
 
0.9980000257450.1%
 
0.996999979530.1%
 
0.9959999919380.1%
 
0.9950000048480.1%
 
0.9940000176390.1%
 
0.9929999709330.1%
 
0.9919999838390.1%
 
0.9909999967540.1%
 

X3
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4969697066534646
Minimum0.0
Maximum1.0
Zeros21
Zeros (%)0.1%
Memory size318.6 KiB
2020-08-25T00:06:00.068356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04899999872
Q10.25
median0.4959999919
Q30.7440000176
95-th percentile0.9480000138
Maximum1
Range1
Interquartile range (IQR)0.4940000176

Descriptive statistics

Standard deviation0.2873078378
Coefficient of variation (CV)0.5781194185
Kurtosis-1.187350915
Mean0.4969697067
Median Absolute Deviation (MAD)0.246999979
Skewness0.009087962138
Sum20260.461
Variance0.08254579366
2020-08-25T00:06:00.174608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.03099999949640.2%
 
0.9789999723630.2%
 
0.112999998610.1%
 
0.8529999852600.1%
 
0.7879999876600.1%
 
0.7129999995570.1%
 
0.4009999931570.1%
 
0.5019999743570.1%
 
0.3350000083560.1%
 
0.3869999945560.1%
 
0.9300000072560.1%
 
0.3199999928560.1%
 
0.8769999743560.1%
 
0.6999999881560.1%
 
0.5469999909550.1%
 
0.5720000267550.1%
 
0.01400000043550.1%
 
0.273999989550.1%
 
0.2980000079550.1%
 
0.324000001550.1%
 
0.3160000145550.1%
 
0.4070000052540.1%
 
0.6340000033540.1%
 
0.2460000068540.1%
 
0.4740000069540.1%
 
Other values (976)3935296.5%
 
ValueCountFrequency (%) 
0210.1%
 
0.001000000047450.1%
 
0.002000000095380.1%
 
0.003000000026410.1%
 
0.00400000019490.1%
 
0.004999999888410.1%
 
0.006000000052480.1%
 
0.007000000216350.1%
 
0.00800000038260.1%
 
0.008999999613400.1%
 
ValueCountFrequency (%) 
1230.1%
 
0.9990000129360.1%
 
0.9980000257340.1%
 
0.996999979380.1%
 
0.9959999919510.1%
 
0.9950000048500.1%
 
0.9940000176470.1%
 
0.9929999709380.1%
 
0.9919999838390.1%
 
0.9909999967360.1%
 

X4
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.500453591091277
Minimum0.0
Maximum1.0
Zeros28
Zeros (%)0.1%
Memory size318.6 KiB
2020-08-25T00:06:00.291678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05000000075
Q10.25
median0.4979999959
Q30.7509999871
95-th percentile0.9499999881
Maximum1
Range1
Interquartile range (IQR)0.5009999871

Descriptive statistics

Standard deviation0.2890108348
Coefficient of variation (CV)0.577497774
Kurtosis-1.202092672
Mean0.5004535911
Median Absolute Deviation (MAD)0.251000002
Skewness-0.001883881862
Sum20402.492
Variance0.08352726265
2020-08-25T00:06:00.397510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.75650.2%
 
0.9139999747620.2%
 
0.4860000014620.2%
 
0.4609999955620.2%
 
0.1770000011600.1%
 
0.3030000031600.1%
 
0.4699999988580.1%
 
0.6579999924580.1%
 
0.8410000205580.1%
 
0.0680000037580.1%
 
0.9649999738570.1%
 
0.8980000019570.1%
 
0.6150000095570.1%
 
0.8379999995560.1%
 
0.9070000052560.1%
 
0.4659999907560.1%
 
0.3350000083560.1%
 
0.8130000234550.1%
 
0.5839999914550.1%
 
0.8059999943550.1%
 
0.2689999938550.1%
 
0.4569999874550.1%
 
0.7839999795550.1%
 
0.6859999895540.1%
 
0.1580000073540.1%
 
Other values (976)3933296.5%
 
ValueCountFrequency (%) 
0280.1%
 
0.001000000047300.1%
 
0.002000000095380.1%
 
0.003000000026380.1%
 
0.00400000019350.1%
 
0.004999999888370.1%
 
0.006000000052410.1%
 
0.007000000216510.1%
 
0.00800000038460.1%
 
0.008999999613460.1%
 
ValueCountFrequency (%) 
119< 0.1%
 
0.9990000129330.1%
 
0.9980000257340.1%
 
0.996999979350.1%
 
0.9959999919380.1%
 
0.9950000048440.1%
 
0.9940000176310.1%
 
0.9929999709470.1%
 
0.9919999838340.1%
 
0.9909999967430.1%
 

X5
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4985624018419907
Minimum0.0
Maximum1.0
Zeros22
Zeros (%)0.1%
Memory size318.6 KiB
2020-08-25T00:06:00.514067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04899999872
Q10.2479999959
median0.4970000088
Q30.75
95-th percentile0.9480000138
Maximum1
Range1
Interquartile range (IQR)0.5020000041

Descriptive statistics

Standard deviation0.2891730148
Coefficient of variation (CV)0.5800136828
Kurtosis-1.203190918
Mean0.4985624018
Median Absolute Deviation (MAD)0.2510000169
Skewness0.002429901133
Sum20325.392
Variance0.08362103249
2020-08-25T00:06:00.621264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.2829999924620.2%
 
0.5120000243620.2%
 
0.2759999931600.1%
 
0.6409999728590.1%
 
0.7919999957590.1%
 
0.9250000119580.1%
 
0.01400000043580.1%
 
0.3059999943580.1%
 
0.898999989580.1%
 
0.8980000019570.1%
 
0.04500000179560.1%
 
0.5189999938560.1%
 
0.0700000003560.1%
 
0.04600000009560.1%
 
0.4250000119550.1%
 
0.3819999993550.1%
 
0.8429999948550.1%
 
0.5799999833550.1%
 
0.4779999852550.1%
 
0.4930000007550.1%
 
0.09000000358540.1%
 
0.4180000126540.1%
 
0.01099999994540.1%
 
0.3379999995540.1%
 
0.6899999976540.1%
 
Other values (976)3935396.5%
 
ValueCountFrequency (%) 
0220.1%
 
0.001000000047360.1%
 
0.002000000095380.1%
 
0.003000000026430.1%
 
0.00400000019460.1%
 
0.004999999888510.1%
 
0.006000000052500.1%
 
0.007000000216420.1%
 
0.00800000038350.1%
 
0.008999999613370.1%
 
ValueCountFrequency (%) 
115< 0.1%
 
0.9990000129430.1%
 
0.9980000257400.1%
 
0.996999979430.1%
 
0.9959999919360.1%
 
0.9950000048420.1%
 
0.9940000176400.1%
 
0.9929999709420.1%
 
0.9919999838460.1%
 
0.9909999967380.1%
 

X6
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5002701874844183
Minimum0.0
Maximum1.0
Zeros19
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:06:00.738724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05000000075
Q10.2509999871
median0.5009999871
Q30.7490000129
95-th percentile0.9480000138
Maximum1
Range1
Interquartile range (IQR)0.4980000257

Descriptive statistics

Standard deviation0.2876367678
Coefficient of variation (CV)0.5749628401
Kurtosis-1.193424759
Mean0.5002701875
Median Absolute Deviation (MAD)0.2489999831
Skewness-0.004213610672
Sum20395.015
Variance0.0827349102
2020-08-25T00:06:00.844664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.1529999971630.2%
 
0.675999999590.1%
 
0.4959999919590.1%
 
0.003000000026580.1%
 
0.201000005570.1%
 
0.199000001570.1%
 
0.1140000001570.1%
 
0.57099998570.1%
 
0.6769999862560.1%
 
0.7360000014560.1%
 
0.03500000015560.1%
 
0.2409999967560.1%
 
0.2939999998560.1%
 
0.3459999859560.1%
 
0.1519999951560.1%
 
0.3849999905560.1%
 
0.8330000043560.1%
 
0.9190000296560.1%
 
0.9169999957550.1%
 
0.9200000167550.1%
 
0.4869999886550.1%
 
0.5910000205550.1%
 
0.5939999819550.1%
 
0.4110000134550.1%
 
0.300999999550.1%
 
Other values (976)3935696.5%
 
ValueCountFrequency (%) 
019< 0.1%
 
0.001000000047460.1%
 
0.002000000095360.1%
 
0.003000000026580.1%
 
0.00400000019440.1%
 
0.004999999888360.1%
 
0.006000000052480.1%
 
0.007000000216290.1%
 
0.00800000038360.1%
 
0.008999999613340.1%
 
ValueCountFrequency (%) 
119< 0.1%
 
0.9990000129370.1%
 
0.9980000257330.1%
 
0.996999979500.1%
 
0.9959999919300.1%
 
0.9950000048440.1%
 
0.9940000176460.1%
 
0.9929999709350.1%
 
0.9919999838410.1%
 
0.9909999967320.1%
 

X7
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5011888246135326
Minimum0.0
Maximum1.0
Zeros15
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:06:00.961802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05000000075
Q10.2529999912
median0.5019999743
Q30.7509999871
95-th percentile0.9499999881
Maximum1
Range1
Interquartile range (IQR)0.4979999959

Descriptive statistics

Standard deviation0.2883729849
Coefficient of variation (CV)0.5753779229
Kurtosis-1.197334192
Mean0.5011888246
Median Absolute Deviation (MAD)0.2490000129
Skewness-0.0001450202439
Sum20432.466
Variance0.08315897842
2020-08-25T00:06:01.069134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.2939999998640.2%
 
0.8379999995620.2%
 
0.03500000015610.1%
 
0.123999998600.1%
 
0.112999998590.1%
 
0.5860000253590.1%
 
0.5939999819580.1%
 
0.5479999781580.1%
 
0.3950000107580.1%
 
0.5139999986580.1%
 
0.2039999962570.1%
 
0.6629999876570.1%
 
0.4959999919570.1%
 
0.5619999766570.1%
 
0.2310000062570.1%
 
0.3740000129570.1%
 
0.6919999719560.1%
 
0.4180000126560.1%
 
0.1959999949560.1%
 
0.5080000162560.1%
 
0.8500000238560.1%
 
0.7009999752550.1%
 
0.7919999957550.1%
 
0.9440000057550.1%
 
0.47299999550.1%
 
Other values (976)3932996.5%
 
ValueCountFrequency (%) 
015< 0.1%
 
0.001000000047360.1%
 
0.002000000095420.1%
 
0.003000000026310.1%
 
0.00400000019540.1%
 
0.004999999888480.1%
 
0.006000000052420.1%
 
0.007000000216360.1%
 
0.00800000038330.1%
 
0.008999999613400.1%
 
ValueCountFrequency (%) 
1270.1%
 
0.9990000129360.1%
 
0.9980000257440.1%
 
0.996999979530.1%
 
0.9959999919390.1%
 
0.9950000048400.1%
 
0.9940000176490.1%
 
0.9929999709340.1%
 
0.9919999838360.1%
 
0.9909999967410.1%
 

X8
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.500311616972539
Minimum0.0
Maximum1.0
Zeros28
Zeros (%)0.1%
Memory size318.6 KiB
2020-08-25T00:06:01.186551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05099999905
Q10.248999998
median0.5019999743
Q30.75
95-th percentile0.9499999881
Maximum1
Range1
Interquartile range (IQR)0.501000002

Descriptive statistics

Standard deviation0.2888849667
Coefficient of variation (CV)0.5774100718
Kurtosis-1.197522298
Mean0.500311617
Median Absolute Deviation (MAD)0.25
Skewness-0.001227680828
Sum20396.704
Variance0.08345452396
2020-08-25T00:06:01.292995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.2759999931680.2%
 
0.4580000043670.2%
 
0.2090000063610.1%
 
0.8169999719580.1%
 
0.5310000181570.1%
 
0.07699999958560.1%
 
0.574000001560.1%
 
0.8159999847560.1%
 
0.1580000073560.1%
 
0.09700000286550.1%
 
0.9900000095550.1%
 
0.871999979550.1%
 
0.7829999924550.1%
 
0.6169999838550.1%
 
0.08799999952540.1%
 
0.9689999819540.1%
 
0.9359999895540.1%
 
0.2440000027540.1%
 
0.2450000048540.1%
 
0.3560000062540.1%
 
0.6579999924540.1%
 
0.08200000226530.1%
 
0.898999989530.1%
 
0.6100000143530.1%
 
0.7049999833530.1%
 
Other values (976)3936896.6%
 
ValueCountFrequency (%) 
0280.1%
 
0.001000000047410.1%
 
0.002000000095420.1%
 
0.003000000026430.1%
 
0.00400000019500.1%
 
0.004999999888420.1%
 
0.006000000052420.1%
 
0.007000000216380.1%
 
0.00800000038390.1%
 
0.008999999613350.1%
 
ValueCountFrequency (%) 
118< 0.1%
 
0.9990000129430.1%
 
0.9980000257420.1%
 
0.996999979480.1%
 
0.9959999919280.1%
 
0.9950000048470.1%
 
0.9940000176510.1%
 
0.9929999709280.1%
 
0.9919999838420.1%
 
0.9909999967460.1%
 

X9
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4971280904914241
Minimum0.0
Maximum1.0
Zeros18
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:06:01.409717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04899999872
Q10.2479999959
median0.4939999878
Q30.746999979
95-th percentile0.949000001
Maximum1
Range1
Interquartile range (IQR)0.4989999831

Descriptive statistics

Standard deviation0.2881554366
Coefficient of variation (CV)0.5796402218
Kurtosis-1.196749519
Mean0.4971280905
Median Absolute Deviation (MAD)0.2489999831
Skewness0.0151352414
Sum20266.91799
Variance0.08303355566
2020-08-25T00:06:01.522442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.008999999613630.2%
 
0.1689999998620.2%
 
0.6629999876610.1%
 
0.773999989600.1%
 
0.949000001600.1%
 
0.1089999974590.1%
 
0.5429999828570.1%
 
0.8679999709570.1%
 
0.2189999968570.1%
 
0.8629999757560.1%
 
0.4979999959560.1%
 
0.7070000172550.1%
 
0.2960000038550.1%
 
0.675999999550.1%
 
0.300999999550.1%
 
0.4769999981550.1%
 
0.6679999828550.1%
 
0.8190000057550.1%
 
0.2860000134540.1%
 
0.925999999540.1%
 
0.009999999776540.1%
 
0.6610000134540.1%
 
0.2879999876540.1%
 
0.06100000069540.1%
 
0.3560000062540.1%
 
Other values (976)3935796.5%
 
ValueCountFrequency (%) 
018< 0.1%
 
0.001000000047420.1%
 
0.002000000095410.1%
 
0.003000000026290.1%
 
0.00400000019420.1%
 
0.004999999888360.1%
 
0.006000000052430.1%
 
0.007000000216400.1%
 
0.00800000038320.1%
 
0.008999999613630.2%
 
ValueCountFrequency (%) 
119< 0.1%
 
0.9990000129410.1%
 
0.9980000257300.1%
 
0.996999979450.1%
 
0.9959999919450.1%
 
0.9950000048430.1%
 
0.9940000176410.1%
 
0.9929999709390.1%
 
0.9919999838450.1%
 
0.9909999967410.1%
 

X10
Real number (ℝ≥0)

Distinct count1001
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5004954375609876
Minimum0.0
Maximum1.0
Zeros19
Zeros (%)< 0.1%
Memory size318.6 KiB
2020-08-25T00:06:01.641442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04899999872
Q10.2520000041
median0.5009999871
Q30.7509999871
95-th percentile0.949000001
Maximum1
Range1
Interquartile range (IQR)0.4989999831

Descriptive statistics

Standard deviation0.2884477619
Coefficient of variation (CV)0.5763244582
Kurtosis-1.198018532
Mean0.5004954376
Median Absolute Deviation (MAD)0.25
Skewness-0.003553635336
Sum20404.198
Variance0.08320211133
2020-08-25T00:06:01.755353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.02400000021630.2%
 
0.3429999948600.1%
 
0.4180000126590.1%
 
0.4110000134590.1%
 
0.09000000358590.1%
 
0.9850000143580.1%
 
0.2099999934570.1%
 
0.5410000086570.1%
 
0.1770000011560.1%
 
0.4189999998560.1%
 
0.08399999887560.1%
 
0.05900000036560.1%
 
0.3670000136550.1%
 
0.3740000129550.1%
 
0.2140000015550.1%
 
0.01899999939550.1%
 
0.1180000007550.1%
 
0.8790000081550.1%
 
0.2899999917540.1%
 
0.5580000281540.1%
 
0.4090000093530.1%
 
0.5569999814530.1%
 
0.4440000057530.1%
 
0.2090000063530.1%
 
0.3199999928530.1%
 
Other values (976)3936996.6%
 
ValueCountFrequency (%) 
019< 0.1%
 
0.001000000047490.1%
 
0.002000000095440.1%
 
0.003000000026270.1%
 
0.00400000019430.1%
 
0.004999999888510.1%
 
0.006000000052480.1%
 
0.007000000216470.1%
 
0.00800000038430.1%
 
0.008999999613330.1%
 
ValueCountFrequency (%) 
118< 0.1%
 
0.9990000129420.1%
 
0.9980000257260.1%
 
0.996999979400.1%
 
0.9959999919360.1%
 
0.9950000048370.1%
 
0.9940000176460.1%
 
0.9929999709460.1%
 
0.9919999838370.1%
 
0.9909999967420.1%
 

target
Real number (ℝ)

Distinct count17396
Unique (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.407828567524549
Minimum-1.2280000448226929
Maximum30.52199935913086
Zeros0
Zeros (%)0.0%
Memory size318.6 KiB
2020-08-25T00:06:02.030220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.228000045
5-th percentile6.10305016
Q110.85099983
median14.42350006
Q317.96125031
95-th percentile22.60794983
Maximum30.52199936
Range31.7499994
Interquartile range (IQR)7.110250473

Descriptive statistics

Standard deviation4.997152837
Coefficient of variation (CV)0.346835945
Kurtosis-0.425629996
Mean14.40782857
Median Absolute Deviation (MAD)3.555500031
Skewness0.001245404621
Sum587378.355
Variance24.97153648
2020-08-25T00:06:02.139593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
17.7099990812< 0.1%
 
15.5310001412< 0.1%
 
14.5179996511< 0.1%
 
14.6579999911< 0.1%
 
15.1780004511< 0.1%
 
13.7229995710< 0.1%
 
10.4040002810< 0.1%
 
12.9309997610< 0.1%
 
16.1299991610< 0.1%
 
17.0079994210< 0.1%
 
18.0100002310< 0.1%
 
16.350999839< 0.1%
 
18.040000929< 0.1%
 
9.9209995279< 0.1%
 
11.973999989< 0.1%
 
13.276000029< 0.1%
 
13.876999869< 0.1%
 
12.590000159< 0.1%
 
14.166000379< 0.1%
 
14.91199979< 0.1%
 
19.170999539< 0.1%
 
14.572999959< 0.1%
 
14.503999719< 0.1%
 
14.736000069< 0.1%
 
12.925999649< 0.1%
 
Other values (17371)4052599.4%
 
ValueCountFrequency (%) 
-1.2280000451< 0.1%
 
-0.52300000191< 0.1%
 
-0.2989999951< 0.1%
 
0.028999999171< 0.1%
 
0.090999998151< 0.1%
 
0.16200000051< 0.1%
 
0.26300001141< 0.1%
 
0.27300000191< 0.1%
 
0.33199998741< 0.1%
 
0.33799999951< 0.1%
 
ValueCountFrequency (%) 
30.521999361< 0.1%
 
30.054000851< 0.1%
 
29.858999251< 0.1%
 
29.752000811< 0.1%
 
29.600000381< 0.1%
 
29.577999111< 0.1%
 
29.554000851< 0.1%
 
29.42600061< 0.1%
 
29.41099931< 0.1%
 
29.361000061< 0.1%
 

Interactions

2020-08-25T00:05:40.548460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:40.691445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:40.832698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:40.973679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.115321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.260093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.404246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.557472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.705003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.850605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:41.996481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.146068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.291973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.431054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.573908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.719403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:42.862875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:43.006259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:43.150374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:43.594266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:43.745668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:43.891330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:44.034829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:44.320782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:44.471412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:44.617736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:44.920206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:45.064274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:45.352445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:45.498528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:45.795928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:45.940925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.085532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.230875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.376244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.523482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.668947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.819663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:46.962555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.110350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.256805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.402667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.546732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.691519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.839807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:47.983951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.132952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.278717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.423465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.571738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.720650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:48.866122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:49.009598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:49.154560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:49.462647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:49.615421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:49.760938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:50.195217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:50.627162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:50.769433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:50.916292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:51.059719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:51.203618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:51.343052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:51.781153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:51.926957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.074485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.216283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.360840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.509248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.659318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.803194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:52.948001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:53.092322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:53.238667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:53.386039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:53.537076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:53.691397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:54.143803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:54.290427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:54.582584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:54.726728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:54.872567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:55.017411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:55.160599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:55.305364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:55.454306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:55.635393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:05:55.970616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.139026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.286494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.434069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.582711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.728012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:56.873696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.023013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.170421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.318664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.467355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.615454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.763777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:57.913086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:58.064810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:58.210508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:58.359789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:58.682753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:58.832953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:06:02.263638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:06:02.484982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:06:02.708314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:06:02.928132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:05:59.118616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:05:59.410555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

X1X2X3X4X5X6X7X8X9X10target
00.4870.0720.0040.8330.7650.6000.1320.8860.0730.34217.948999
10.2230.4010.6590.5280.8430.7130.5800.4730.5720.52813.815000
20.9030.9130.9400.9790.5610.7440.6270.8180.3090.51020.766001
30.7910.8570.3590.8440.1550.9480.1140.2920.4120.99118.301001
40.3260.5930.0850.9270.9260.6330.4310.3260.0310.73022.989000
50.5620.8900.0060.6910.7200.2080.2790.2830.1160.88225.986000
60.4810.6130.4990.5720.9140.7830.2040.4280.8280.48717.150000
70.6250.1970.7250.6280.5410.4810.4600.0210.7650.39214.006000
80.2100.5190.0290.6100.7240.5150.3710.7310.5750.73018.566000
90.0840.4960.4860.8130.4060.4910.4180.3440.9780.40912.107000

Last rows

X1X2X3X4X5X6X7X8X9X10target
407580.8270.5310.9390.3370.1070.2280.8610.5530.0110.02716.594999
407590.0780.0200.8510.5430.6140.2720.5920.0010.1930.62810.087000
407600.8950.8170.6720.3040.5300.1520.2390.7060.1360.70513.575000
407610.5510.8250.8130.4510.9540.6790.4680.0880.8790.30220.125999
407620.4870.1020.4950.4180.0670.2620.4450.8980.1520.8456.958000
407630.9110.9600.2050.2240.7030.5310.0611.0000.3370.4448.797000
407640.6730.5330.0860.0970.1230.4050.6740.7520.7720.62414.518000
407650.0520.6440.6450.8380.4660.3600.1770.8490.1530.68311.611000
407660.8350.0740.7800.9160.6210.4100.3420.4660.8960.44715.360000
407670.5370.4900.1690.6380.7260.6050.9070.3940.4950.99119.325001